Mirror Security has collaborated with NVIDIA to deliver GPU-accelerated fully homomorphic encryption (FHE) for AI inference, enabling encrypted computation for sensitive workloads in regulated sectors.
AI adoption in regulated sectors has long been constrained by a structural vulnerability: data must typically be decrypted during inference.
Mirror Security, a research-driven cybersecurity company spun out of University College Dublin, has announced a collaboration with NVIDIA to address that gap. The partnership integrates GPU-accelerated fully homomorphic encryption (FHE) into AI inference pipelines, allowing computation to occur while data remains encrypted.
The development targets industries such as government, healthcare, finance, and other sectors subject to stringent regulatory oversight.
Closing the “decryption window”
Traditional AI inference workflows require encrypted data to be decrypted before model processing, creating what security experts often describe as a “vulnerability window.”
For regulated entities, this introduces compliance challenges related to:
- Personally identifiable information (PII)
- Proprietary business intelligence
- Embedded decision logic
- Sensitive operational signals
Fully homomorphic encryption allows computation on encrypted data without exposing raw information at any stage.
Mirror Security’s platform integrates across NVIDIA’s accelerated computing stack, including CUDA, cuBLAS, the NeMo framework with NeMo Retriever, and TensorRT-LLM for optimized model serving.
The objective: enable secure AI inference and secure AI memory where both processing and storage remain encrypted.
Sovereign AI as a policy priority
Beyond compliance, the partnership taps into a broader geopolitical theme: sovereign AI.
Governments and enterprises increasingly seek control over:
- Data jurisdiction
- Encryption keys
- Model governance
- Infrastructure sovereignty
Mirror Security’s leadership frames sovereignty not merely as data residency but as cryptographic control over intelligence execution.
As cross-border AI deployments expand, encrypted inference could reduce reliance on trust-based frameworks by replacing them with cryptographic guarantees.
GPU acceleration solves FHE’s historical bottleneck
Fully homomorphic encryption has long been viewed as theoretically powerful but computationally expensive.
GPU acceleration is critical to making FHE practical at inference scale.
By leveraging NVIDIA’s hardware and software ecosystem, the collaboration aims to reduce latency and performance penalties traditionally associated with encrypted computation.
If successful, the integration could lower one of the key barriers to enterprise adoption of encrypted AI.
Regulated sectors under pressure
Healthcare providers, financial institutions, and government agencies face mounting scrutiny over data handling practices.
AI systems operating in these sectors must navigate:
- Data protection laws
- Cross-border data transfer restrictions
- Cybersecurity mandates
- Audit and compliance frameworks
Encrypted inference offers a potential pathway for deploying advanced AI without exposing underlying data assets.
Startup ecosystem implications
Mirror Security, backed by Sure Valley Ventures and Atlantic Bridge, operates across India, Ireland, and the United States.
Its collaboration with NVIDIA reflects a broader trend: startups building cryptographic layers atop hyperscale AI infrastructure.
For NVIDIA, partnerships with security-focused startups reinforce its position not only as a compute provider but as a foundational layer for regulated AI deployments.
The company’s Inception program and VC Alliance initiatives aim to accelerate such ecosystem integrations.
Enterprise adoption pathway
Despite the promise, adoption will depend on:
- Performance benchmarking
- Integration simplicity
- Cost structure
- Regulatory validation
Enterprises must evaluate whether encrypted inference meets latency and throughput requirements for production workloads.
Proof-of-concept deployments in highly regulated environments will likely serve as early test cases.
A structural inflection for AI security
As AI systems handle increasingly sensitive data, traditional perimeter-based security approaches are proving insufficient.
Encrypted AI computation shifts security from reactive defense to mathematical assurance.
If GPU-accelerated FHE scales effectively, it could redefine how organizations approach AI governance.
For regulated industries, the question is no longer whether to adopt AI.
It is how to do so without compromising compliance or control.
Mirror Security’s collaboration with NVIDIA positions encrypted inference as one possible answer.
In the AI era, security may not depend on where data sits — but on whether it ever needs to be seen at all.
